Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts

Last update: Jan 07, 2023

Overview

DataSelection-NMT

Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts

Quick update: The paper got accepted on Dec 6, 2021! I will link the repository to the paper as soon as it got published.

Our Pre-trained models on Hugging Face

Systems	Link	Systems	Link
Top1	Download	Top1	Download
Top2+Top1	Download	Top2	Download
Top3+Top2+...	Download	Top3	Donwload
Top4+Top3+...	Download	Top4	Donwload
Top5+Top4+...	Download	Top5	Donwload
Top6+Top5+...	Download	Top6	Donwload

How to use

Note: we ported the best checkpoints of trained models to the Hugging Face (HF). Since our models were trained by OpenNMT-py, it was not possible to employ them directly for inference on HF. To bypass this issue, we use CTranslate2– an inference engine for transformer models.

Follow steps below to translate your sentences:

1. Install the Python package:

pip install --upgrade pip
pip install ctranslate2

2. Download models from our HF repository: You can do this manually or use the following python script:

import requests

url = "Download Link"
model_path = "Model Path"
r = requests.get(url, allow_redirects=True)
open(model_path, 'wb').write(r.content)

3. Convert the downloaded model:

ct2-opennmt-py-converter --model_path model_path --output_dir output_directory

3. Translate tokenized inputs:

Note: the inputs should be tokenized by SentencePiece. You can also use tokenized version of IWSLT test sets.

import ctranslate2
translator = ctranslate2.Translator("output_directory/")
translator.translate_batch([["▁H", "ello", "▁world", "!"]])

import ctranslate2
translator = ctranslate2.Translator("output_directory/")
translator.translate_file(input_file, output_file, batch_type= "tokens/examples")

To customize the CTranslate2 functions, read this API document.

4. Detokenize the outputs:

Note: you need to detokenize the output with the same sentencepiece model as used in step 3.

tools/detokenize.perl -no-escape -l fr \
< output_file \
> output_file.detok

5. Remove the @@ tokens:

cat output_file.detok | sed -E 's/(@@)|(@@ )|(@@ ?$)//g' \
> output._file.detok.postprocessd

Use grep to check if @@ tokens removed successfully:

grep @@ output._file.detok.postprocessd

Authors

Javad Pourmostafa - Email, Website
Dimitar Shterionov - Email, Website
Pieter Spronck - Email, Website

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Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts

Related tags

Overview

DataSelection-NMT

Quick update: The paper got accepted on Dec 6, 2021! I will link the repository to the paper as soon as it got published.

Our Pre-trained models on Hugging Face

How to use

Authors

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Releases(1.1)

1.1(Oct 25, 2021)

Owner

Javad Pourmostafa

All supplementary material used by me while TA-ing CS3244: Machine Learning

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